#include <algorithm>
#include <cfloat>
#include <vector>

#include "caffe/layers/adapted_weighted_softmax_with_loss_layer.hpp"
#include "caffe/util/math_functions.hpp"

namespace caffe {

template <typename Dtype>
void AdaptedWeightedSoftmaxWithLossLayer<Dtype>::LayerSetUp(
    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
  LossLayer<Dtype>::LayerSetUp(bottom, top);
  LayerParameter softmax_param(this->layer_param_);
  softmax_param.set_type("Softmax");
  softmax_layer_ = LayerRegistry<Dtype>::CreateLayer(softmax_param);
  softmax_bottom_vec_.clear();
  softmax_bottom_vec_.push_back(bottom[0]);
  softmax_top_vec_.clear();
  softmax_top_vec_.push_back(&prob_);
  softmax_layer_->SetUp(softmax_bottom_vec_, softmax_top_vec_);
}

template <typename Dtype>
void AdaptedWeightedSoftmaxWithLossLayer<Dtype>::Reshape(
    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
  LossLayer<Dtype>::Reshape(bottom, top);
  softmax_layer_->Reshape(softmax_bottom_vec_, softmax_top_vec_);
  softmax_axis_ =
      bottom[0]->CanonicalAxisIndex(this->layer_param_.softmax_param().axis());
  outer_num_ = bottom[0]->count(0, softmax_axis_);
  inner_num_ = bottom[0]->count(softmax_axis_ + 1);
  CHECK_EQ(outer_num_ * inner_num_, bottom[1]->count())
      << "Number of labels must match number of predictions; "
      << "e.g., if softmax axis == 1 and prediction shape is (N, C, H, W), "
      << "label count (number of labels) must be N*H*W, "
      << "with integer values in {0, 1, ..., C-1}.";
  if (top.size() >= 2) {
    // softmax output
    top[1]->ReshapeLike(*bottom[0]);
  }
}

template <typename Dtype>
void AdaptedWeightedSoftmaxWithLossLayer<Dtype>::Forward_cpu(
    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
  // The forward pass computes the softmax prob values.
  softmax_layer_->Forward(softmax_bottom_vec_, softmax_top_vec_);
  const Dtype* prob_data = prob_.cpu_data();
  const Dtype* label = bottom[1]->cpu_data();
  int dim = prob_.count() / outer_num_;
  int count = 0;
  Dtype loss = 0;
  Dtype loss_pos = 0;
  Dtype loss_neg = 0;
  Dtype temp_loss_pos = 0;
  Dtype temp_loss_neg = 0;
  Dtype count_pos = 0;
  Dtype count_neg = 0;
  
  for (int i = 0; i < outer_num_; ++i) {
    temp_loss_pos = 0;
    temp_loss_neg = 0;
    count_neg = 0;
    count_pos = 0;
    for (int j = 0; j < inner_num_; j++) {
      const int label_value = static_cast<int>(label[i * inner_num_ + j]);
      DCHECK_GE(label_value, 0);
      DCHECK_LT(label_value, prob_.shape(softmax_axis_));
      if(label_value>0){
        count_pos++;
      	temp_loss_pos -= log(std::max(prob_data[i * dim + label_value * inner_num_ + j],
                         Dtype(FLT_MIN)));
      }else{
	count_neg++;
      	temp_loss_neg -= log(std::max(prob_data[i * dim + label_value * inner_num_ + j],
                         Dtype(FLT_MIN)));
      }
      ++count;
    }

    weight_pos = count_neg / (count_pos + count_neg);
    loss_pos += temp_loss_pos * weight_pos;
    weight_neg = count_pos / (count_pos + count_neg);
    loss_neg += temp_loss_neg * weight_neg;
  }
  top[0]->mutable_cpu_data()[0] = (loss_pos + loss_neg) / (Dtype)count;
  if (top.size() == 2) {
    top[1]->ShareData(prob_);
  }
}

template <typename Dtype>
void AdaptedWeightedSoftmaxWithLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
  if (propagate_down[1]) {
    LOG(FATAL) << this->type()
               << " Layer cannot backpropagate to label inputs.";
  }
  if (propagate_down[0]) {
    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
    const Dtype* prob_data = prob_.cpu_data();
    caffe_copy(prob_.count(), prob_data, bottom_diff);
    const Dtype* label = bottom[1]->cpu_data();
    int dim = prob_.count() / outer_num_;
    int count = 0;
    Dtype count_pos = 0;
    Dtype count_neg = 0;
    for (int i = 0; i < outer_num_; ++i) {
      for (int j = 0; j < inner_num_; ++j) {
        const int label_value = static_cast<int>(label[i * inner_num_ + j]);
	if(label_value > 0) count_pos++;
	else count_neg++;
	}
  	weight_pos = count_neg / (count_pos + count_neg);
  	weight_neg = count_pos / (count_pos + count_neg);
      
      for (int j = 0; j < inner_num_; ++j) {
        const int label_value = static_cast<int>(label[i * inner_num_ + j]);
          bottom_diff[i * dim + label_value * inner_num_ + j] -= 1;
          Dtype w = (label_value > 0) ? weight_pos : weight_neg;
          for (int k = 0; k < bottom[0]->shape(softmax_axis_); ++k) {
            bottom_diff[i * dim + k * inner_num_ + j] *= w;
          }
          ++count;
        }
      }
    // Scale gradient
    Dtype loss_weight = top[0]->cpu_diff()[0] / count;
    caffe_scal(prob_.count(), loss_weight, bottom_diff);
  }
}

#ifdef CPU_ONLY
STUB_GPU(AdaptedWeightedSoftmaxWithLossLayer);
#endif

INSTANTIATE_CLASS(AdaptedWeightedSoftmaxWithLossLayer);
REGISTER_LAYER_CLASS(AdaptedWeightedSoftmaxWithLoss);

}  // namespace caffe
